Understanding Classification in Data Mining Classification involves creating a model to predict the class of objects with unknown labels. It categorizes new data based on current or past information, such as grouping patients by medical history. The process includes two steps: model construction and usage. Model construction uses training data for algorithms to learn patterns and generate a classifier model, which then applies these learned rules during the usage phase.
Prediction Techniques Explained Prediction estimates missing or unknown values using existing data trends, producing continuous rather than discrete outputs. For instance, it determines appropriate treatments for patients based on their health conditions like diabetes levels. This method relies heavily on analyzing historical and present datasets to forecast outcomes effectively.